Instructions to use AXKuhta/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AXKuhta/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AXKuhta/bert-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AXKuhta/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("AXKuhta/bert-finetuned-ner") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- Model Preparation Time: 0.0021
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```
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precision recall f1-score support
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B-COMMENT 0.88 0.90 0.89 767
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B-NAME 0.91 0.91 0.91 1050
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- Model Preparation Time: 0.0021
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```
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precision recall f1-score support
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B-COMMENT 0.88 0.90 0.89 767
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B-NAME 0.91 0.91 0.91 1050
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## Training procedure
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See bert_finetune_fin.ipynb
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### Training hyperparameters
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The following hyperparameters were used during training:
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